pySATSI: A Python Package for Computing Focal Mechanism Stress Inversions

Abstract We introduce pySATSI, a Python package for computing earthquake focal mechanism stress inversions. This algorithm can handle a wide variety of types of stress inversion problems with a single script and can duplicate many capabilities of preceding methodologies. We also add new capabilities that include spatiotemporally variable inversion grids, damped stress estimates for clusters with few or no focal mechanisms, and variable fault-plane ambiguities that the user can assign to individual events. In addition, we added the ability to use damped stress inversions with fault-plane ambiguity probabilities that are weighted by fault instabilities. Our algorithm is computationally efficient with faster runtimes than previous algorithms, scales well for large datasets, and can be easily parallelized.

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Publication Details

Journal
Seismological Research Letters
Published
2026-01-26
DOI
https://doi.org/10.1785/0220250182
Citations
2
Primary Topic
earthquake and tectonic studies
Type
article
Field-Weighted Citation Impact
109.50
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article

pySATSI: A Python Package for Computing Focal Mechanism Stress Inversions

Jeanne L. Hardebeck, Andrew J. Michael, Robert J. Skoumal
2 citations
Seismological Research Letters
earthquake and tectonic studies
109.50
article

pySATSI: A Python Package for Computing Focal Mechanism Stress Inversions

Jeanne L. Hardebeck, Andrew J. Michael, Robert J. Skoumal
article en
2 citations

Abstract

Abstract We introduce pySATSI, a Python package for computing earthquake focal mechanism stress inversions. This algorithm can handle a wide variety of types of stress inversion problems with a single script and can duplicate many capabilities of preceding methodologies. We also add new capabilities that include spatiotemporally variable inversion grids, damped stress estimates for clusters with few or no focal mechanisms, and variable fault-plane ambiguities that the user can assign to individual events. In addition, we added the ability to use damped stress inversions with fault-plane ambiguity probabilities that are weighted by fault instabilities. Our algorithm is computationally efficient with faster runtimes than previous algorithms, scales well for large datasets, and can be easily parallelized.

Seismological Research Letters
United States Geological Survey (US)
Openalex Percentile: Top 0%
earthquake and tectonic studies
109.50
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